Savinay Nagendra
Computer Vision Engineer Perception Algorithms at Apple
San Francisco Bay Area United States
Summary
👤
Senior🎓
Top SchoolSavinay Nagendra is a research scientist and Ph.D. graduate in Computer Science and Engineering from Penn State, specializing in computer vision with eight years of industry and academic experience across semantic and instance segmentation, few-shot prompting, and privacy-aware model interpretability. He has led vision projects funded by Google AI, built a few-shot spatial prompt engineering system for Meta’s Segment Anything Model with Schlumberger, and designed multimodal video LLMs and segmentation tools during industry stints at Meta and SLB. His work spans deep learning for high-resolution and satellite imagery, 3D generative modeling, reinforcement learning for oilfield optimization, and developing robust models under differentially private SGD. Based in the San Francisco Bay Area, he combines solid system-building experience with research depth—often translating prototypes into deployed or patent-pursuing pipelines—bringing a rare mix of applied ML engineering and rigorous academic insight.
8 years of coding experience
8 years of employment as a software developer
Bachelor of Engineering - BE Electrical and Electronics Engineering, Bachelor of Engineering - BE Electrical and Electronics Engineering at PES UniversityDoctor of Philosophy - PhD Computer Science and Engineering, Doctor of Philosophy - PhD Computer Science and Engineering at Penn State University
Stackoverflow
1reputation
0reached
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Github Skills (15)
q-learning10
swing9
function-approximation9
actor-critic9
sarsa8
algorithms8
angular8
reinforcement-learning8
python7
pre-commit-hook7
autopep86
gofmt6
yapf5
segmentation4
binarization3
Programming languages (3)
Github contributions (5)
savinay95n/Behavioral-cloning-Steering-angle-prediction
May 2021 - May 2022
Contributions:1 release, 11 commits, 10 pushes in 1 year
savinay95n/Reinforcement-learning-Algorithms-and-Dynamic-Programming
Jan 2018 - May 2022
Reinforcement learning Algorithms such as SARSA, Q learning, Actor-Critic Policy Gradient and Value Function Approximation were applied to stabilize an inverted pendulum system and achieve optimal control. So essentially, the concept of Reinforcement Learning Controllers has been established. The Reinforcement Learning Controllers have been compared on the basis of performance and efficiency and they are separately compared with the classical Linear Quadratic Regulator Controller. Each of the RL controller have been integrated with a Swing up controller. A virtual switch toggles between the Swing up controller and the RL controller automatically, based on the value of the angular deviation theta with respect to the vertical plane. My research paper and my undergraduate thesis have been uploaded for reference. All the codes have also been uploaded.
Contributions:1 release, 5 commits, 4 pushes in 4 years 5 months
actor-criticangularfunction-approximationq-learningreinforcement-learning
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